R E S E A R C H A R T I C L E
Open Access
Avoiding Catch-22: validating the
PainDETECT in a in a population of
patients with chronic pain
Hans Timmerman
1*, André P. Wolff
2, Ewald M. Bronkhorst
3, Oliver H. G. Wilder-Smith
1,4, Marcel J. Schenkels
5,
Nick T. van Dasselaar
6, Frank J. P. M. Huygen
7, Monique A. H. Steegers
1and Kris C. P. Vissers
1Abstract
Background: Neuropathic pain is defined as pain caused by a lesion or disease of the somatosensory nervous system and is a major therapeutic challenge. Several screening tools have been developed to help physicians detect patients with neuropathic pain. These have typically been validated in populations pre-stratified for neuropathic pain, leading to a so called“Catch-22 situation:” “a problematic situation for which the only solution is denied by a circumstance inherent in the problem or by a rule”. The validity of screening tools needs to be proven in patients with pain who were not pre-stratified on basis of the target outcome: neuropathic pain or non-neuropathic pain. This study aims to assess the validity of the Dutch PainDETECT (PainDETECT-Dlv) in a large
population of patients with chronic pain.
Methods: A cross-sectional multicentre design was used to assess PainDETECT-Dlvvalidity. Included where patients with
low back pain radiating into the leg(s), patients with neck-shoulder-arm pain and patients with pain due to a suspected peripheral nerve damage. Patients’ pain was classified as having a neuropathic pain component (yes/no) by two experienced physicians (“gold standard”). Physician opinion based on the Grading System was a secondary comparison. Results: In total, 291 patients were included. Primary analysis was done on patients where both physicians agreed upon the pain classification (n = 228). Compared to the physician’s classification, PainDETECT-Dlvhad a sensitivity of 80%
and specificity of 55%, versus the Grading System it achieved 74 and 46%.
Conclusion: Despite its internal consistency and test-retest reliability the PainDETECT-Dlvis not an effective screening
tool for a neuropathic pain component in a population of patients with chronic pain because of its moderate sensitivity and low specificity. Moreover, the indiscriminate use of the PainDETECT-Dlv as a surrogate for clinical
assessment should be avoided in daily clinical practice as well as in (clinical-) research. Catch-22 situations in the validation of screening tools can be prevented by not pre-stratifying the patients on basis of the target outcome before inclusion in a validation study for screening instruments.
Trial registration: The protocol was registered prospectively in the Dutch National Trial Register:NTR 3030.
Keywords: PainDETECT questionnaire, Reliability, Validity, Sensitivity, Specificity, Screening tool, Neuropathic pain, Pain, Clinical assessment, Low back pain, Neck shoulder arm pain, Peripheral nerve damage
* Correspondence:hans.timmerman@radboudumc.nl
1Department of Anesthesiology, Pain and Palliative Medicine, Radboud
university medical center, Huispost 549, PO Box 9101, 6500, HB, Nijmegen, the Netherlands
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Background
The International Association for the Study of Pain defines neuropathic pain as“pain caused by a lesion or disease of the somatosensory nervous system” and states that “neuro-pathic pain is not a medical diagnosis but a clinical de-scription which requires a demonstrable lesion or a disease that satisfies established neurological diagnostic criteria” [1]. In the clinical context it is better to speak of a present or an absent neuropathic pain component (present- or ab-sent- NePC) with respect to so called mixed-pain condi-tions [2,3] in which neuropathic pain and nociceptive pain both exist. Clinically, NePC is considered to manifest spe-cific symptoms and signs [4,5]. The classification of NePC is usually based on history and physical examination in-cluding (bedside-) sensory testing [6,7]. The correct classi-fication of NePC is important for patients because NePC has a considerable impact on the quality of daily life [8] and for physicians since the treatment differs strongly from that of patients without NePC [6,9,10].
An easy to use and validated screening tool for clinical triage and epidemiological purposes could aid uniform classification and quantification of NePC and hence lead
to better therapy, particularly when used by
non-specialists [6–8,11–15].
The PainDETECT is such a patient friendly screening tool for the screening for neuropathic pain. It was origin-ally developed and validated in Germany [2] based on two groups of patients (patients with pain of predominantly neuropathic origin or of predominantly nociceptive origin) with at least a 40% score on a visual analogue scale for pain (VAS;0–100). The gold standard used in this study was the assessment of the pain type based on the examin-ation by two experienced pain specialists. This resulted in a percentage of correctly identified patients of 83% for neuropathic pain, a sensitivity of 85 and 80% specificity [2]. Subsequently, validation studies were performed in Spain [16], Turkey [17], Japan [18], India (Hindi) [19] and Korea [20]. Since the introduction of the PainDETECT this instrument has been used in many clinical and epi-demiological studies [21]. In a Danish study, based on
PainDETECT outcome, NePC was present [22] in about
40% of the patients with musculoskeletal pain.
In the above-mentioned validation studies [2, 16–20], the validity of the PainDETECT as a screening tool was performed in pre-stratified groups of patients based on the target outcome (pain of predominantly neuropathic origin or of predominantly nociceptive origin and limita-tion to pain scores). The inclusion of only patients with a known pain classification on forehand might lead to a prerequisite for the determination of validity of the Pain-DETECT. For this situation, the term“Catch-22” is used in the English language for“ a problematic situation for which the only solution is denied by a circumstance in-herent in the problem or by a rule” [23]. It was firstly
described in Joseph Heller’s novel Catch-22 which de-scribes a general situation in which an individual has to accomplish two actions that are mutually dependent on the other action that must be completed first.
The objective of this study is to further validate the Pain-DETECT as a screening tool for use in daily outpatient practices for detecting a NePC. The current validation study is being conducted in a general patient population having common chronic pain syndromes, not pre-stratified on the target outcome: low back with leg pain (LBLP), neck-shoulder-arm pain (NSA pain) or a suspected periph-eral nerve damage pain (suspected PND pain).
Methods
The study was conducted in a cross-sectional, observa-tional, research design with two weeks and three months follow up to study the clinimetric quality (i.e. reliability and validity) of the PainDETECT. This study, to detect a NePC in patients suffering from chronic pain, was approved by the medical and ethical review board Committee on Research Involving Human Subjects re-gion Arnhem-Nijmegen, Nijmegen, the Netherlands, Dossier number: 2008/348; NL 25343.091.08 and con-ducted in accordance with the declaration of Helsinki and the declaration of the World Medical Association. As required, written informed consent was obtained from patients prior to study participation. The protocol is regis-tered in the Dutch National Trial Register: NTR 3030. The PainDETECT was translated and cross-culturally adapted
into the Dutch language (PainDETECT -Dlv) (© Pfizer
Pharma GmbH 2005, Pfizer bv 2008. Cappelle a/d IJssel, the Netherlands) in a separate study [24] before the com-mencement of the present validation study. In this study, the same methodology was used as in the previously pub-lished protocol [25] and as employed in a simultaneous study regarding the validity of the DN4 [26] .
Patients
The patients were recruited from October 2009 until July 2013. Multicenter recruitment took place in the Netherlands in three academic centers specialized in pain medicine, three non-academic centers specialized in pain medicine and one non-academic department of neurology. The question to participate in the study was asked by the patients’ own physician. At that moment they only had a provisional diagnosis: LBLP, NSA pain or pain due to a suspected PND (Conditions associated with a lesion of the peripheral somatosensory system). These three groups of patients include a majority of the patients referred towards an academic or peripheral pain clinic from the general practitioner. Patients had to be diagnosed for the initial cause of the pain as classified according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision
(ICD-10)-2015-WHO Version 2015 [27]. Importantly, pa-tients were not pre-stratified on the target outcome: the existence of NePC yes or no [28]. Patients, when willing to participate, were included when they met the following inclusion criteria: Male or female adult patients (> 18 years of age) with chronic (≥3 months) LBLP or NSA pain radi-ating into leg (s) or arm (s) respectively or patients with chronic pain due to a suspected PND. Exclusion criteria were: Patients diagnosed with an active malignant dis-order, compression fractures, patients with diffuse pains (pains with an origin in muscles, bones or joints: such as fibromyalgia or ankylosing spondylitis), severe mental ill-ness, chronic alcoholism or substance abuse, inability to fill in the questionnaire adequately or incapable of under-standing the Dutch language.
Physicians’ assessment
Patients were examined for the presence of NePC by
two physicians which was considered to be the “gold
standard” in this study. The physicians (pain specialists, pain specialist in training or neurologists always oper-ated in differently composed pairs) worked independ-ently from each other and were blinded to the classification made by the other physician. The physi-cians were not selected on basis of age, years of experi-ence as a physician or other criteria. A full medical history was taken followed by a thorough clinical exam-ination. A bedside examination (touch, pin prick, pres-sure, cold, heat and temporal summation) to assess
patients’ pain [25] was based on the European
Federation of Neurological Societies (EFNS) guidelines [29, 30], the IASP Neuropathic Pain Special Interest Group (NeuPSIG) guidelines on neuropathic pain as-sessment [6] and the guidelines for assessment of
neuro-pathic pain in primary care [7]. Patients’ pain was
classified by the physician as pain with present- or absent-NePC. The NeuPSIG Grading System for neuro-pathic pain as proposed by Treede et al. [31] was used as a secondary comparison with the outcome of the PainDETECT-Dlv. The assessment of the Grading System
was implemented in the standardized assessment proto-col and thus included in the diagnostic work-up of the patients [25]. The outcomes “probable” and “definite”
were regarded as “present-NePC”. “Unlikely” and
“pos-sible” were rated as “absent-NePC” [32–34]. All partici-pating physicians underwent standard medical training, belonging to the classic medical curriculum, and exam-ination of the (central) nervous system in particular. To achieve standardization of history and assessment of NePC presence in patients included in this study all par-ticipating physicians underwent a training in the per-formance of the clinical examination of the patients (including sensory (bedside) examination and use of the NeupSIG Grading System) [25]. Training of the physicians
took place at the participating center. During the execu-tion of the study, the study coordinator (HT) visited the participating centers on a regularly basis to answer ques-tions, to see if the necessary equipment was always avail-able and to keep an eye on the inclusion of patients. Based on the order of assessment, the physician who performed the first assessment was called physician A and the phys-ician who performed the assessment as a second physphys-ician was named physician B. However, the order of the physi-cians was based on availability during the study.
PainDETECT-Dlvand other questionnaires
The PainDETECT-Dlv (© Pfizer Pharma GmbH 2005,
Pfizer bv 2008. Cappelle a/d IJssel, the Netherlands) [2,24] was designed as a simple, patient self-administered screen-ing tool to screen for the presence of neuropathic pain without physical examination. This instrument consists of one item about the pain course pattern, one about radiat-ing pain and seven items about the gradation of pain. An overall score is generated and ranges between− 1 and 38. Additionally, there are three items about pain severity (current, worst and average pain) included in the PainDE-TECT. For the original German version [2] the outcome was as follows: ‘-1 - 12: negative’, neuropathic pain is un-likely; 13–18: ‘unclear’; result is ambiguous, however neuropathic pain can be present; 19–38 ‘positive’, neuro-pathic pain is likely.
The patient completed five questionnaires (including the PainDETECT-Dlvdirectly after the clinical assessment
by the participating physicians but without any interfer-ence by the physicians. The researcher (HT) was avail-able for help via telephone or in person when it was not clear how to fill in the questionnaires. Besides screening for NePC via the PainDETECT-Dlv [24], the disability of
the patient was assessed via the Disability Rating Index (DRI) [35]. The existence of an anxiety disorder and/or depression were assessed via the Hospital Anxiety Depression Scale (HADS) [36–38] and the Pain Attribu-tion Scale (PAS) was used to assess patients attribuAttribu-tion of his or hers pain. Quality of life was determined via
the RAND 36-item Health Survey (RAND-36) [39–41].
Two weeks and three months after the initial visit the follow-up questionnaires (the Patients Global Impression of Change (PGIC) [42–44] and the PainDETECT-Dlv)
were sent to the patient by mail.
Data
All data gathered from patients and physicians was collected on paper and stored at the Radboudumc, Nijmegen, The Netherlands. Data management and monitoring were performed within MACRO (MACRO, version 4.1.1.3720, Infermed, London, United Kingdom).
Statistical methods
Power calculation for this study was based on a expected NePC prevalence of 37% in an unselected cohort of pa-tients with chronic low back pain [2]. Sensitivity and spe-cificity of the PainDETECT were assessed in the original validation study as respectively 85 and 80% [2]. The sensi-tivity and specificity of the PainDETECT-Dlvwas, on
fore-hand, expected to be 80% with a prevalence of 37%. The lower 95% confidence limit was required to be > 0.55. According to the calculations following the formulas by Flahault et al. [45] 132 patients with LBLP, NSA pain or suspected PND pain were needed so that the sample size contained a sufficient numbers of cases and controls [25].
Qualitative variables were presented as frequencies and percentages. The quantitative variables were pre-sented as mean and standard deviation (SD) or as median and inter quartile range (IQR). Based on the classifications of the two physicians, all patients were
categorized as absent-NePC, NePC or ‘undetermined’
(i.e. the classification by both physicians jointly was not equal).
One-way ANOVA (with additional Tukey’s studentized range post-hoc test) or Kruskal-Wallis test were used to study differences between the three groups (NePC, absent-NePC, Undetermined).
Intraclass correlation (ICC) was used to assess repro-ducibility (‘test-retest reliability’) of the PainDETECT-Dlv
between the fixed time points (baseline versus two weeks & baseline versus three months).The ICC and
re-sponsiveness of the PainDETECT-Dlv were assessed
be-tween each point of measurement.
A receiver operating characteristic (ROC) curve was calculated and the area under the curve (AUC) with 95% confidence interval is presented to indicate the
discrim-inatory power of the PainDETECT-Dlv to discriminate
patients classified as with or without a NePC. The classi-fication was based on the physicians’ assessment out-come or based on the Grading System outout-come, respectively. The theoretical maximum of the AUC is 100%, indicating a perfect discrimination and 50% is equal to tossing a coin. The optimal cut-off point of the
PainDETECT-Dlv – sum score was calculated under the
condition of equal-costs of misclassification, using the Youden-index. Sensitivity, specificity, positive and nega-tive predicnega-tive values and the likelihood ratio in the population in this study was calculated at this cut-off point. Also, the‘number needed to diagnose (NND)’ was assessed [46] by use of the formula: NND = 1/[Sensitivity – (1-specificity)]. A clinical screening tool for the dem-onstration of a neuropathic pain component is consid-ered valid if it has a high sensitivity, specificity and a high positive predictive value. For the measurement of the usefulness of the screening tool the likelihood ratio will be used [47].
The agreement between the pain classification by the physicians, the NeuPSIG Grading Systems and the Pain-DETECT-Dlv(yes:≥11, no:< 11) outcome was evaluated by
using Cohen’s kappa (K), prevalence index (Pi) and per-centage of pair wise agreement (PA) [25]. AΚ ≥ 0.40 and a PA≥ 70% is considered indicative of interobserver reliabil-ity which is acceptable for use in clinical practice [48].
Data analysis and statistics were performed by use of Statistical Package for the Social Sciences (SPSS version 20.0, SPSS Inc., Chicago, Illinois, USA). Two-tailed p-value below 0.05 was considered statistically significant. Results
Patient population
In this study 330 patients, not pre-stratified on the target outcome, with chronic LBLP, NSA pain or suspected PND pain were assessed for eligibility. Two patients did not give their informed consent. Exclusion (n = 37) was due to not fulfilling the in- and exclusion criteria (n = 13); not return-ing the baseline questionnaires by the patient (n = 16); missing pain classification by one physician (n = 5) or both physicians (n = 3). In eight patients the assessment of the grading system (secondary comparison) was missing by one or both physicians. Finally, 291 patients participated in the study between October 2009 and July 2013. Accord-ing to the international classification of diseases (ICD-10, version 2015) [27] these patients were classified as follows: 8 patients suffered from pain related to endocrine, nutri-tional and metabolic diseases (chapter IV); 75 patients from diseases of the nervous system (chapter VI); 1 pa-tient from diseases of the circulatory system (chapter IX);189 patients from diseases of the musculoskeletal sys-tem and connective tissue (chapter XIII); 1 patient from diseases of the genito-urinary system (chapter XIV); 3 pa-tients from symptoms, signs and ill-defined conditions, and 14 patient from injury, poisoning or other conse-quences of external causes.
Numbers of recruitment in the different participat-ing hospitals (all in the Netherlands) were as follows: Reinier de Graaf Gasthuis, Delft n = 86; ErasmusMC,
Rotterdam n= 62; Radboudumc, Nijmegen n= 59;
Bernhoven Ziekenhuis, Oss n = 56; Rijnstate Ziekenhuis, Arnhem n = 15; St. Anna ziekenhuis, Geldrop n = 12 and UMC Utrecht, Utrecht n = 1. 132 patients had LBLP with radiation in one or two legs (45.4%), 51 NSA pain with ra-diation into one or both arms (17.5%) and 108 (37.1%) had suspected PND pain. The group of patients with sus-pected PND consisted of 86 patients with pain who were treated because of breast cancer (surgery and/or radiation and/or chemotherapy and/or hormonal therapy). The remaining 22 patients had pain because of various rea-sons: peripheral nerve damage (n = 12), polyneuropathy (n = 3), central post stroke pain (n = 2), Complex Regional Pain Syndrom (n = 2) and spinal radicular pain (n = 3).
After assessment by physicians A and physicians B, 170 patients were classified as having present-NePC, 58 as absent-NePC. In 63 patients the two physicians made a non-concordant pain classification, so the outcome based on the physicians assessment was classified as ‘undeter-mined’. Based on the NeuPSIG Grading System in 139 pa-tients NePC was classified as present, in 93 papa-tients NePC was absent and in 51 patients the two physicians made a non-concordant pain classification in which the outcome was classified as‘undetermined’ (see Fig.1: Flow Diagram). Social-demographic and clinical details of the 291 pa-tients were analyzed and divided from each other based on the pain classification (see Table 1). No statistically significant differences were found between absent-NePC, present-NePC and undetermined for gender, age, height, weight, body mass index (BMI), education, medication, duration of pain, quality of life, disability, pain attribu-tion, anxiety disorder and depression. Moreover, no sta-tistically significant differences were observed between absent-NePC and present-NePC for pain (current, worst and average pain).
Physicians
During this study 62 various physicians (pain specialist, pain specialist-fellow or neurologist), from seven differ-ent hospitals, assessed all included patidiffer-ents. All patidiffer-ents were assessed two times by two different physicians. Of all participating physicians, 21 physicians assessed ≤2
patients during the execution of the study, 23 physicians
saw ≤9 patients, 10 physicians saw ≥10 patients and 8
physicians saw≥20 patients.
Evaluation of the PainDETECT-Dlv
The mean score of the PainDETECT-Dlv (Range− 1;38)
for patients classified as absent-NePC was 10.7 (SD ± 5.7); for patients classified as present-NePC it was 15.7 (SD ± 6.3) and for patients with an undetermined out-come it was 11.8 (SD ± 5). As calculated based on a
one-way ANOVA with Tukey’s studentized range
post-hoc test, there was a statistical significant difference between absent-NePC and present-NePC (P < 0.001) and between present-NePC and undetermined (P < 0.001). No significant difference was seen between absent-NePC and undetermined (P = 0.57). Patients pain course pat-tern and if the pain was radiating to other regions of the body were not significantly different between the three groups. Pain descriptors (burning, tingling or prickling, painful light touching, sudden pain attacks, temperature evoked pain, numbness sensation and pressure evoked pain) were all statistically significant discriminators for
the presence of NePC (P≤ 0.005) except for pressure
evoked pain (P = 0.07). See Table 2 for the
PainDE-TECT-Dlvoutcomes divided according to the pain
classi-fication by the physicians (present- NePC, absent-NePC or undetermined) (See Table2).
Fig. 1 Flow diagram for the validation of the PainDETECT-Dlv. PA: Physicians’ assessment; GS: Neuropathic pain special interest group Grading
System; Present-NePC: Neuropathic pain component present; Undetermined: both physicians disagree with each other about the presence of a neuropathic pain component; Absent-NePC: No neuropathic pain component present; n = number of patients in analysis
Table 1 Socio-demographic and clinical characteristics of the patients related to physicians agreement for the existence of a NePC
NePC Absent Present Undetermined
N n (%) N n (%) N n (%)
Mean (±SD) Mean (±SD) Mean (±SD)
Median [IQR] Median [IQR] Median [IQR]
Gender 58 170 63 Male 25 (43%) 56 (33%) 17 (27%) Female 33 (57%) 114 (67%) 46 (73%) Age (Years) 58 55 ± 12 170 56 ± 11 63 58 ± 13 Height (cm) 55 172 ± 9 164 172 ± 8 62 170 ± 9 Weight (kg) 55 84 ± 25 167 80 ± 17 62 80 ± 16 BMI (kg/m2) 54 28 ± 8 164 27 ± 5 62 27 ± 5 Education 56 164 63 Functional illiterate 0 (0%) 0 (0%) 2 (3.3%) Primary education 2 (3.6%) 14 (8.5%) 6 (9.8%) Secondary education 32 (57.1%) 98 (59.8%) 38 (62.3%) Postgraduate 22 (39.3%) 52 (31.7%) 15 (24.6%) Medication (% yes) 55 31 (56.9%) 168 111 (66.1%) 61 35 (57.4%) Pain (NRS; 0–10) Current pain 57 5 [3–7] 167 6 [3–7] 61 4 [1–7]
Worst pain (past four weeks) 57 8 [5–9] 167 8 [7–9] 61 7 [5–8]
Average pain (past four weeks) 57 6 [3.5–7] 167 6 [5–8] 61 6 [3–7]
Duration of pain (months) 57 72 ± 90 169 60 ± 76 62 49 ± 46
Quality of life
Physical functioning 58 57 ± 27 170 51 ± 25 62 55 ± 29
Role functioning physical 58 43 ± 42 170 35 ± 41 61 41 ± 45
Role functioning emotional 58 80 ± 35 169 70 ± 43 61 73 ± 42
Social functioning 58 43 ± 14 170 44 ± 11 62 46 ± 10 Bodily pain 58 55 ± 24 170 56 ± 25 62 46 ± 25 Mental health 58 60 ± 6 170 61 ± 10 61 62 ± 7 Vitality 58 51 ± 10 170 49 ± 12 61 50 ± 11 General health 57 58 ± 14 165 57 ± 14 60 55 ± 12 Health change 58 38 ± 24 170 40 ± 26 63 42 ± 27 Disability Total 53 46 ± 27 158 48 ± 24 57 40 ± 26 Pain attribution Somatic 53 5.2 ± 4.3 156 6.0 ± 4.0 58 5.2 ± 3.9 Psychological 58 2.0 ± 2.9 164 2.2 ± 3.2 60 2.9 ± 3.0 Social 57 1.6 ± 2.2 163 2.0 ± 2.6 61 2.4 ± 2.6 Anxiety disorder 57 14 (24.6%) 167 46 (27.5%) 60 18 (30.0%) Depression 57 14 (24.6%) 166 46 (27.7%) 61 11 (18.0%)
Classification for the existence of NePC is based on physicians assessment of the patients
NePC: neuropathic pain component; Absent: NePC is absent; Present: NePC is present; Undetermined: both physicians disagree with each other about the existence of a neuropathic pain component; N: total number of patients in analysis; n: number of patients; %: percentage; SD: Standard deviation; IQR: Inter quartile range
Validity
The gold standard for presence of the NePC in this study was the concordant opinion of both physicians. On basis of this gold standard, patients with an identical pain classi-fication were included in the initial analysis (n = 228): 58 patients were classified as absent-NePC (25.4%) and 170 were classified as present-NePC (74.6%)(see Table 3 and Additional file1: Table S1). A ROC-curve was constructed for PainDETECT-Dlv(see Fig.2). Based on the gold
stand-ard, PainDETECT-Dlv sensitivity was (at maximal
Youden-index) 80%, specificity 55.2%, positive predictive value 84% and the positive likelihood ratio was 1.78. Based on the neuropathic pain Grading System, the sensitivity was 74.1%, specificity 46.2%, positive predictive value 67.3%, and positive likelihood ratio of 1.38.
We also constructed ROC-curves for the classification by solely physicians A or B and according to the neuro-pathic pain Grading System by physicians A or B and all the combinations. Except for classification of patients’ pain based on the description of physicians A and the outcome of the Grading System by physicians B all cut-off scores were calculated at 11-points out of 38: The sensitivity ranges from 57.6–86.1% and specificity from 43.9–59.2%. The classification of patients’ pain based on the classification of physicians A resulted in a cut-off score of 9-points: Sensitivity 86.1% and specificity
45.8%. The classification of patients’ pain based on the Grading System according to physicians B resulted in a cut-off score of 14-points: Sensitivity of 57.6% and specifi-city of 59.2%. In Table3and Additional file1: Table S1 we present the number of patients with LBLP, NSA pain or suspected PND pain, in total and per group based on phy-sicians’ assessment and/or the Grading System. Values of the AUC, cut-off value, sensitivity and specificity are pro-vided (see also Additional file 1: Table S1 for a more
de-tailed analysis of the diagnostic accuracy of the
PainDETECT-Dlv: AUC, cut-off value, sensitivity,
specifi-city positive and negative predictive values, positive likeli-hood ratios and the number needed to diagnose (NND).
Patients were screened on a NePC (positive outcome) by two physicians, two times the Grading System, and the patient completed the PainDETECT-Dlv. All the possible
outcome combinations were computed based on the out-come: Is a NePC present, or not? In 283 patients all the five outcome variables were available and are displayed in a Venn-diagram [49] (see Fig. 3). In 92 patients (32.5%), five times a positive outcome variable was found, indicat-ing presence of NePC. In 23 patients all outcome variables were negative (8.1%), thus indicating absence of NePC. One positive outcome was detected in 39 patients (13.8%), two positive outcomes in 28 patients (9.9%), three in 49 patients (17.3%), and four in 52 patients (18.4%).
Table 2 The median (IQR) of the items of the PainDETECT by physicians agreement for the existence of a NePC
NePC Absent Present Undetermined
PainDETECT item N n (%) N n (%) N n (%)
Median [IQR] Median [IQR] Median [IQR]
Mean (±SD) Mean (±SD) Mean (±SD)
Pain course pattern 58 162 59
Persistent pain with slight fluctuations 19 (33%) 53 (33%) 17 (29%)
Persistent pain with pain attacks 14 (24%) 58 (36%) 17 (29%)
Pain attacks without pain between them 16 (28%) 32 (20%) 20 (34%)
Pain attacks with pain between them 9 (16%) 18 (11%) 5 (9%)
Radiating pain (% yes) 51 41 (78%) 154 112 (73%) 57 38 (67%)
Gradation of pain
Burning 55 0 [0–2] 170 1 [0–3] 62 0 [0–2.25]
Tingling or prickling 55 1 [0–3] 170 2 [0–3] 63 1 [0–3]
Painful light touching 55 0 [0–1] 169 1 [0–2] 63 0 [0–1]
Sudden pain attacks 55 2 [0–3] 167 3 [1–4] 62 2 [0–3]
Temperature evoked pain 54 0 [0–1] 170 1 [0–2] 63 1 [0–1]
Numbness sensation 56 2 [0–3] 170 3 [2–4] 63 3 [1–4]
Pressure evoked pain 55 2 [1–3] 170 3 [1–4] 63 2 [1–3]
Total sum score PainDETECT 58 10 [6.75–15.25] 170 16 [11–20] 63 10 [8–15]
10.7 (± 5.75) 15.7 (± 6.3) 11.8 (± 5)
Classification of NePC is based on physicians assessment of the patients
NePC: neuropathic pain component; Absent: NePC is absent; Present: NePC is present; Undetermined: both physicians disagree with each other about the existence of a neuropathic pain component; N = total number of patients in analysis; n = number of patients; IQR: inter quartile range; SD: standard deviation; Range: 0 = never; 1 = hardly noticed; 2 = slightly; 3 = moderately; 4 = strongly; 5 = very strongly; Total sum score PainDETECT: Sum score calculation of the PainDETECT
Reliability
To determine the interobserver reliability between the physicians, the Grading System and the outcome of the PainDETECT-Dlvfor the classification of a (absent-) NePC,
Cohen’s kappa (K) and percentage of pair wise agreement (PA) were assessed (see Table4) K for the classification of patients’ pain (absent-NePC or NePC) by the physicians was 0.49, with a PA of 78.4% (Pi = 0.38; n = 291). The K for the classification of patients’ pain based on the Grad-ing System was 0.63 and PA was 82% (Pi = 0.16; n = 283).
The outcome of K and PA regarding the PainDETECT-Dlv
compared to the classification of physicians A was re-spectively 0.34 and 74.6% (Pi = 0.48; n = 291). Compared to physicians B it was 0.27 and 67.7% (Pi = 0.33; n = 291).
Comparing the outcome of the PainDETECT-Dlv to
the outcome of the Grading System, was 0.18 and 61.5%(Pi = 0.27; n = 286) for physicians A, and 0.17 and 58.3%(Pi = 0.05; n = 288) for physicians B.
Stability and responsiveness of the PainDETECT-Dlv
over time was assessed over a period of two weeks. The
mean sum score of the PainDETECT-Dlvat baseline for
the total group was 13.8 ± 6.3. The mean sum score after two weeks was 14.1 ± 6.1. Test-retest reliability via ICC was 0.83 (95%CI 0.79–0.87; n = 268). Taking into consid-eration the fact that patients’ pain should not have chan-ged (outcome based on the PGIC), because otherwise the ICC would not reflect the consistency of the
Table 3 The AUC and the sensitivity / specificity at the optimal cut-off point of the PainDETECT under the condition of equal costs of misclassification to classify NePC by the classification and the Grading System of the physicians
PainDETECT versus classification by: NePC: AUC % (95%CI) Cut -off Sens % (95% CI) Spec % (95% CI)
Absent (n) Present (n) Assessment A 83 208 69.8 (0.63–0.77) 9 86.0 (0.81–0.90) 45.8 (0.36–0.57) Assessment B 96 195 67.2 (0.61–0.74) 11 75.4 (0.69–0.81) 52.1 (0.42–0.62) Assessment A = Assessment B 58 170 72.1 (0.65–0.80) 11 80.0 (0.73–0.85) 55.2 (0.43–0.67) LBLP 28 75 75.4 (0.65–0.86) 11 84.0 (0.74–0.91) 64.3 (0.46–0.79) NSA pain 18 23 62.9 (0.46–0.80) 9 82.6 (0.63–0.93) 44.4 (0.25–0.66) Suspected PND pain 12 72 75.5 (0.63–0.88) 15 55.6 (0.44–0.67) 91.7 (0.65–0.99) Grading A 114 172 58.9 (0.52–0.66) 11 73.3 (0.66–0.79) 43.9 (0.35–0.53) Grading B 130 158 58.6 (0.52–0.65) 14 57.6 (0.50–0.65) 59.2 (0.51–0.67) Grading A = Grading B 93 139 61.3 (0.54–0.69) 11 74.1 (0.66–0.81) 46.2 (0.37–0.56) LBLP 60 48 63.1 (0.52–0.74) 12 70.8 (0.57–0.82) 55.0 (0.43–0.67) NSA pain 24 13 48.9 (0.28–0.69) 18 30.8 (0.13–0.58) 87.5 (0.69–0.96) Suspected PND pain 9 78 66.0 (0.45–0.87) 13 64.1 (0.53–0.74) 77.8 (0.45–0.94) Assessment A = Grading A 63 155 69.1 (0.61–0.77) 11 76.1 (0.69–0.82) 55.6 (0.43–0.67) Assessment B = Grading B 77 139 67.1 (0.60–0.74) 11 76.3 (0.69–0.83) 52.0 (0.41–0.63)
Assessment A = Grading A = Assessment B = Grading B
43 118 67.9 (0.59–0.77) 11 78.0 (0.70–0.85) 53.0 (0.39–0.68)
Classification of NePC is based on (both) physicians’ assessment of the patients and / or on (both) the NeuPSIG Grading Systems
NEPC: Neuropathic pain component existing; Absent NePC: Neuropathic pain component not existing; AUC: Area under curve; 95%CI: 95% confidence interval; Cut-off: Cut-off value; Sens.: Sensitivity; Spec.: Specificity; LBLP: Patients with low back and leg pain; NSA pain: Patients with neck-shoulder-arm pain; suspected PND pain: Patients with pain due to a suspected peripheral nerve damage
1-Specificity
Sensit
ivit
y
Fig. 2 Receiver operating characteristics and area under the curve (AUC) for the total score of the PainDETECT-Dlvversus the presence of a
neuropathic pain component as classified by two physicians (n = 228; undetermined patients are not included). X-axis: 1-Specifity; Y-axis: Sensitivity
PainDETECT-Dlv, and a time gap of 7–21 days was allowed
(to rule out the early or delayed return of questionnaires) between the first and second PainDETECT-Dlv, the ICC
was 0.87 (95% CI 0.81–0.91; n = 123). After three months, with no change in the degree of patients’ pain and a time gap of 60–120 days between the first and third PainDE-TECT-Dlv, ICC was 0.86 (95% CI 0.79–0.91; n = 102).
Discussion
This study demonstrates the clinimetric quality of the
PainDETECT-Dlv, a screening instrument for the
presence of a NePC, on a large population of patients, with chronic pain due to low back with leg pain, neck-shoulder-arm pain or pain due to a suspected per-ipheral nerve damage as normally present in a physi-cian’s daily practice. Because the patients were included without pre-stratification on the target outcome, previ-ous Catch-22 situations in the assessment of the validity of screening instruments were avoided. Under these conditions, the PainDETECT-Dlv failed to be predictive
for the existence of a NePC due to a moderate sensitivity and low specificity, irrespective of comparison with the expert opinion via the classification by two physicians
(gold standard) as well as with the outcome of the NeuPSIG Grading System. Moreover, the predictive values were also not indicating that the PainDETECT-Dlv
is a valid screening tool for the assessment of a NePC. The likelihood ratios were also not suggestive for the usefulness of this instrument.
Validation studies with patients pre-stratified for NePC
We found an optimal cut-off score for the PainDE-TECT-Dlv of≥11 points corresponding to a sensitivity of
80% and a specificity of 55%. In the original develop-ment and validation study of the PainDETECT by Freynhagen et al. [2] a sensitivity and specificity of 84% was found. The gold standard in their study was the examination by two experienced pain specialists. The study was performed at ten different specialized pain centers. Only patients with‘typical’ neuropathic or noci-ceptive entities (i.e. no ‘unclear’ outcome) and only pa-tients with a VAS of > 40 mm (0 - 100 mm) were included. In the Spanish validation study by De Andrés
et al. [16] only patients with a VAS ≥40 mm and a
known classification (by one experienced specialist) of neuropathic pain, mixed pain or nociceptive pain were
Absent-NePC: n = 23 Assessment B (n = 251) Grading A (n=246) Grading B (n=244) PainDETECT (n=219) Assessment A (n=204)
Fig. 3 VENN-Diagram of all the five outcomes per patient. Physicians A: classification of a neuropathic pain component (NePC) exists; Physicians B: NePC exists; Grading A: NePC exists according to the Grading System by physicians A; Grading B: NePC exists according to the Grading System by physicians B; PainDETECT-Dlv: Outcome of the PainDETECT-Dlvindicates the existence of a NePC. Non NePC: No NePC exists according to
included. It revealed a sensitivity and specificity of 81% when patients with the classification of neuropathic pain or nociceptive pain were included. The inclusion of pa-tients with mixed pain in the neuropathic pain group re-sulted in a sensitivity of 84% and specificity of 78%. The
Korean version of the PainDETECT [20] was validated
based on the study by De Andrés [16] in patients with
chronic pain and with a NRS≥ 3 (NRS 0–10). The gold
standard was the independent diagnosis of the patient by two experienced pain physicians. It revealed a sensitivity of 82% and a specificity of 92% based on a cut-off score of ≥19 (range − 1; 38). In the validation of the Turkish version of the PainDETECT [17] patients were included with the classification of pain type (i.e. NePC) being assessed beforehand (based on the opinion of two expert pain physicians) and patients suffering from pain of three centimeters or more (VAS 0–10 cm). Sensitivity and specificity were respectively 78 and 83%. The Hindi
version of the PainDETECT [19] was validated in
patients with neuropathic and in patients with
non-neuropathic pain based on a conventional single as-sessment by one physician. At a optimal cut off point of ≥18 sensitivity was 83% and the specificity was 84%.
In a cohort of patients with a spinal cord injury for more than one year, pain lasting more than six months and a pain intensity of than more three on a NRS (0–10) a sensi-tivity was found of 68% and specificity of 83% [50].
The present study included patients with chronic pain without limits to the minimal pain intensity or other limitations. At the moment of inclusion in the study our
patients had only a provisional diagnosis (LBLP, NSA pain, suspected PND pain) established in primary or sec-ondary care without further refinement or confirmation. Then, after referral to a (non-) academic pain clinic, they were assessed as to their complaints for the first time at
study inclusion. Thus this was a ‘real-life’ clinical
out-patient population. Avoiding patient selection due to pre-stratification to the outcome target makes our study unique and clinically more relevant as compared to other studies on the same topic and is crucial for the validation of a screening instrument.
Validation studies with patients not pre-stratified for NePC
In a study by Gauffin et al. [51] in patients diagnosed with fibromyalgia (n = 158) a cut-off score of 17 was found with a sensitivity of 79% and specificity of 53% (Gold standard: the classification by one experienced physician). This study, like ours, did not pre-stratify tients according to the pain classification either, and pa-tients were not excluded because of a low pain level. The outcome of Gauffin’s sensitivity analyses in this fibromyalgia study was comparable to our study. Tampin et al. [34] found, based on the examination by a physical therapist, a sensitivity and specificity of the PainDETECT of respectively 64 and 62% (cut-off score 18.5) in a population of patients with neck/upper limb pain (n = 122). In our study, the outcome for patients with neck-shoulder-arm pain was 83 and 44% respect-ively (cut-off score of≥9).
Table 4 The kappa coefficient between the classification on basis of the assessment by the physicians, the Grading Systems and the PainDETECT
PainDETECT (yes / no) Grading A Grading B Assessment B
Assessment A n 291 286 288 291 K 0.34 0.48 0.32 0.49 PA 74.6 76.2 67.4 78.4 Pi 0.48 0.32 0.26 0.38 Assessment B n 291 286 288 K 0.27 0.38 0.48 PA 67.7 71.0 75.0 Pi 0.33 0.28 0.22 Grading A n 286 283 286 K 0.18 0.63 0.38 PA 61.5 82.0 71.0 Pi 0.27 0.16 0.28 Grading B n 288 K 0.17 PA 58.3 Pi 0.05
Classification of NePC is based on physicians’ assessment of the patients and on the Grading Systems
Grading system
In this study the physicians assessed patients for the presence of a NePC according to the Grading System [31]. Probable neuropathic pain and definite neuropathic pain were combined as present-NePC, and non neuro-pathic pain and possible neuroneuro-pathic pain were com-bined in absent-NePC. Sensitivity and specificity of the PainDETECT-Dlv resulted to be 74 and 46% respectively
(Cut-off score 11 out of 38, n = 232). Using the classifica-tion of patients’ pain based solely on the Grading System by one physician results in a lower validity than based on the physicians assessment. This might suggest that the classification of patients’ pain based on the Grading System is less accurate than the classification based on the physicians’ assessment in respect to the outcome of
the PainDETECT-Dlv. However, the grading system was
assessed by the same physician who also performed the physician’s assessment so it is also possible that the physician had difficulties to classify patients pain based on the Grading System or vice versa. When using the physicians’ assessments as well as the Grading Systems of both physicians (n = 161), sensitivity was 78%, specifi-city was 53% and the cut-off score was 11: The same poor result as for the gold standard. In the papers by Vaegter et al. [33] and Tampin et al. [34] the
PainDE-TECT was also compared with the NeuPSIG Grading
System. In both papers, like in ours, the outcome of the Grading System was not comparable to the outcome of the PainDETECT. As stated by Finnerup et al. [52] the PainDETECT (and other screening tools for the assess-ment of neuropathic pain) is only to alert the physician to further assess the patient who may have a NePC.
NePC classification
The initial classification of patients’ pain in our study was based on an interview and (clinical/physical) examination by trained (pain-) physicians. There is a lack of consensus with respect to the classification of a NePC in patients with pain of different origins [53]. Moreover, a lack of standardization of assessment methods increases the num-ber of undetected or poorly classified patients which leads to a variation in the classification accuracy (i.e. sensitivity and specificity) of screening tools caused by differences in strategy and patient population [15, 54]. Bouhassira and Attal recently stated that neuropathic pain is“a consistent clinical entity, but it is multidimensional in terms of its clinical expression, with different sensory profiles, poten-tially reflecting specific pathophysiological mechanisms” [55]. As stated by Scholz et al. [53] physical tests are more useful to identify patients with neuropathic back pain than interview questions. To reach a more unified classification system to differentiate between present-NePC and absent-NePC a standardized assessment of symptoms and signs is necessary [53]. However, these tests are not able to
confirm the relation between the potential lesion or dis-ease of the nerve and the pain directly: The classification of neuropathic pain should be based on clinical examin-ation and the interpretexamin-ation should be placed in the clin-ical context of patients’ pain [55].
In this study we used a mandatory standardized as-sessment [25] in addition to the medical history and physical examination which were performed according to the physicians’ standards. The clinical assessment and the use of the Grading System showed that in 18–22% of the patients a non-consistent assessments was present
resulting in an ‘undetermined’ status. In Freynhagens
paper [2] it was 5%. This difference might be due to the inclusion of patients with a less clear absent or present NePC in our study which might reflect what happens in the assessment of a NePC in usual clinical care. More-over, this also might occur in the treatment of patients with chronic pain. Based on both the physician’s assess-ments, almost 75% of the patients in this study had a neuropathic pain component. This might be due to sev-eral facts. (1) Patients with LBLP or NSA pain were only included when the pain was radiating into the leg(−s) re-spectively the arm(−s) and were not removed from this study when they had mixed pain. Moreover, patients with radiating pain are more suspected to have a NePC. (2) There is a possibility that neuroplastic changes are interpreted as neuropathy in patients with chronic LBLP. (3) Patients were recruited in secondary and tertiary pain clinics. This might have led to a inclusion of patients who were more difficult to treat in primary care and (4) we included 108 patients with suspected peripheral nerve damage. Almost 60% of the patients after treat-ment for breast cancer has pain [56]. Based on the re-cent review by Ilhan et al. [57], in patients who reported pain following breast cancer treatment the pooled preva-lence of neuropathic pain from screening questionnaires ranged from 32.6 to 58.2%. Following the NeuPSIG Grading System the prevalence ranged from 29.5 to 57.1%. Based on these numbers, patients after breast cancer can be regarded as patients suspected of neuro-pathic pain due to peripheral nerve damage. However,
the PainDETECT-Dlv (compared to the gold standard
and the NeuPSIG Grading System) as used in our study seems not valid for the assessment of patients with neuropathic pain based on a suspected PND in which the majority of patients was suffering of pain after treat-ment for breast cancer.
Strengths and weaknesses
There are several strengths in this study. Firstly, we in-cluded a large population of patients with diagnoses who are regularly seen in daily clinical practice. Secondly, there was no pre-stratification on the target outcome, clear inclusion criteria and almost no exclusion criteria.
Thirdly, we used the NeuPSIG Grading System [31,52] as a secondary comparison. The main purpose of the Grading System is to help in the classification of the
pain as neuropathic [52]. In our study, the Grading
System was added to the standardized assessment form which had to be filled in by the physician. There are also some weaknesses in this study. The use of the Grading
System within the clinical assessment (including
bed-side examination) is a strong aspect of our study, but the outcome of the clinical examination as well as the outcome of the Grading System might be influenced by each other. However, combining the physicians’ as-sessment with the Grading System might have made the ‘gold standard’ even stronger but also might have led to a cross-contamination. Secondly, diagnosing NePC by assessing patients’ pain by two separate physicians in
our and in other studies is considered as the ‘Gold
Standard’. However, classifying patients’ pain may be done more objectively by establishing a damaged nerve and by diagnosticating in a more detailed clinical way. Moreover, the breakdown of clinical grounds for in- and exclusion could also have been assessed and captured in more detail. Thirdly, 62 physicians participated. This might have led to the inclusion of younger, less clinical experienced physicians. However, it reflects ‘real life’ practice and limits the risk of systematic bias in the clas-sification of patients’ pain and bias based on assump-tions about the existence of a NePC. Moreover, all physicians followed the standardized training as de-scribed. Fourthly, almost only patients with peripheral causes of pain were selected. This can be considered as a methodological drawback. Moreover, because we did not include patients with, by example, low back pain without irradiation to the leg who would probably be diagnosed as absent-NePC the specificity might decrease. Fifthly, there is an apparent lack of objective tests to determine whether the somatosensory fibers were affected, in par-ticular the small fibers. This can be seen as crucial since objective data are mandatory to reach a definite neuro-pathic pain classification in the grading system. Lastly, in a following study we would collect data from the pa-tients who were not able to participate in the study to prevent inclusion bias. In this study this was not possible because of ethical regulations.
Conclusions
The PainDETECT-Dlv has a good internal consistency
and test-retest reliability but is not an effective screening tool for the assessment of a neuropathic pain component in a population of patients with chronic pain, irrespect-ive of the chosen comparison because of its moderate sensitivity and low specificity. However, the agreement by both the physicians and the agreement with the grad-ing systems (performed by the physicians) were also not
impressive. Moreover, the differences in the cut-off scores for the different comparisons reflects the fact that agreement in a not pre-stratified to the target outcome patient population is not easy to accomplish. Using the PainDETECT-Dlv (for screening purposes or as a
surro-gate for clinical assessment) may result in unreliably sep-arating NePC presence from non-presence in patients with chronic pain in clinical outpatient practices and in research settings. Catch-22 situations in the validation of screening tools can be prevented by not pre-stratifying the patients on basis of the target outcome before inclu-sion in a validation study for screening instruments. For now, classifying patients pain still needs the clinical as-sessment based on history and physical examination in-cluding bed-side sensory testing by the physician and cannot be replaced by the use of the PainDETECT. Additional file
Additional file 1:Table S1. The AUC and the sensitivity / specificity at the optimal cut-off point of the PainDETECT under the condition of equal costs of misclassification to classify a NePC by the diagnosis and the grading system of the physicians for the total group and according the pain locations. (PDF 423 kb)
Abbreviations
AUC:Area Under the Curve; BMI: Body Mass Index; CI: Confidence Interval; DRI: Disability Rating Index; EFNS: European Federation of Neurological Societies; HADS: Hospital Anxiety and Depression Scale; IASP: International Association for the Study of Pain; ICC: Intraclass Correlation; ICD-10: International Statistical Classification of Diseases and Related Health Problems 10th revision; IQR : Inter Quartile Range; K: Cohen’s kappa; LBLP: Low Back with Leg Pain; NePC: Neuropathic Pain Component; NeuPSIG: Neuropathic Pain Special Interest Group; NND: Number Needed to Diagnose; NRS: Numeric Rating Scale; NSA pain: Neck-Shoulder-Arm pain; NTR: National Trial register; PA: Percentage of Agreement; PainDETECT -Dlv: PainDETECT Dutch language version; PAS: Pain Attribution Scale;
PGIC: Patient Global Impression of Change; Pi: Prevalence index;
PND: Peripheral nerve damage pain; RAND-36: RAND 36-item health Survey; ROC: Receiver Operating Characteristic; SD: Standard Deviation;
SPSS: Statistical package for the Social Sciences; VAS: Visual Analogue Scale; WHO: World Health Organization
Acknowledgements
Thanks to all the participating patients for their invaluable work to this study. Also thanks to the participating physicians and assistants of Rijnstate Ziekenhuis, Arnhem; Bernhoven Ziekenhuis, Oss; St.Anna Ziekenhuis, Geldrop; Reinier de Graaf Gasthuis, Delft; Utrecht University Medical Center, Utrecht; Erasmus Medical Center, Rotterdam and Radboud university medical center, Nijmegen. We would like to thank Jan Hendriks for his help with the statistics until his retirement.
Funding
This study was performed within DALI for PAIN, a national program that focuses on neuropathic pain care optimalisation. DALI for PAIN is an initiative of Pfizer. This project is supported by an unrestricted grant from Pfizer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors’ contributions
Conception and Design was performed by HT, AW, OWS, KV. Provision of study materials or patients and the collection and assembly of data was done by HT, MS, MS, NvD, FH. Data analysis was performed by HT, AW, EB, OWS, KV. All authors discussed the results and commented on the manuscript. The final manuscript was approved by all authors. Ethics approval and consent to participate
This study was approved by the medical and ethical review board Committee on Research Involving Human Subjects region Arnhem-Nijmegen, Arnhem-Nijmegen, the Netherlands, Dossier number: 2008/348; NL 25343.091.08 and conducted in accordance with the declaration of Helsinki and the declaration of the World Medical Association. As required, written informed consent was obtained from patients prior to study participation. The protocol is registered in the Dutch National Trial Register: NTR 3030. Consent for publication
Not applicable. Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author details
1Department of Anesthesiology, Pain and Palliative Medicine, Radboud
university medical center, Huispost 549, PO Box 9101, 6500, HB, Nijmegen, the Netherlands.2Department of Anesthesiology, Pain Center, University of
Groningen, University medical center Groningen, Groningen, the Netherlands.3Department for Health Evidence, Radboud university medical
center, Nijmegen, the Netherlands.4Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark.5Bernhoven Ziekenhuis, Department
of Anesthesiology, Uden, the Netherlands.6Reinier de Graaf Gasthuis, Department of Anesthesiology, Pain Medicine and Palliative Care, Delft, the Netherlands.7ErasmusMC, Department of Anesthesiology, University Center of Pain Medicine, Rotterdam, the Netherlands.
Received: 24 April 2017 Accepted: 22 June 2018 References
1. IASP Taxonomy Neuropathic Pain.http://www.iasp-pain.org/Education/ Content.aspx?ItemNumber=1698#Neuropathicpain. Accessed 26 June 2018. 2. Freynhagen R, Baron R, Gockel U, Tolle TR. painDETECT: a new screening
questionnaire to identify neuropathic components in patients with back pain. Curr Med Res Opin. 2006;22(10):1911–20.
3. La Cesa S, Tamburin S, Tugnoli V, Sandrini G, Paolucci S, Lacerenza M, Marchettini P, Cruccu G, Truini A. How to diagnose neuropathic pain? The contribution from clinical examination, pain questionnaires and diagnostic tests. Neurol Sci : official J of the Italian Neurol Soc and of the Ital Soc of Clin Neurophysiol. 2015;36(12):2169–75.
4. Vissers KC. The clinical challenge of chronic neuropathic pain. Disabil Rehabil. 2006;28(6):343–9.
5. Baron R, Binder A, Wasner G. Neuropathic pain: diagnosis,
pathophysiological mechanisms, and treatment. The Lancet Neurol. 2010; 9(8):807–19.
6. Haanpaa M, Attal N, Backonja M, Baron R, Bennett M, Bouhassira D, Cruccu G, Hansson P, Haythornthwaite JA, Iannetti GD, et al. NeuPSIG guidelines on neuropathic pain assessment. Pain. 2011;152(1):14–27.
7. Haanpaa ML, Backonja MM, Bennett MI, Bouhassira D, Cruccu G, Hansson PT, Jensen TS, Kauppila T, Rice AS, Smith BH, et al. Assessment of neuropathic pain in primary care. Am J Med. 2009;122(10 Suppl):S13–21. 8. Dieleman JP, Kerklaan J, Huygen FJ, Bouma PA, Sturkenboom MC. Incidence
rates and treatment of neuropathic pain conditions in the general population. Pain. 2008;137(3):681–8.
9. O'Connor AB, Dworkin RH. Treatment of neuropathic pain: an overview of recent guidelines. Am J Med. 2009;122(10 Suppl):S22–32.
10. Dworkin RH, O'Connor AB, Kent J, Mackey SC, Raja SN, Stacey BR, Levy RM, Backonja M, Baron R, Harke H, et al. Interventional management of neuropathic pain: NeuPSIG recommendations. Pain. 2013;154(11):2249–61. 11. Bennett MI, Attal N, Backonja MM, Baron R, Bouhassira D, Freynhagen R,
Scholz J, Tolle TR, Wittchen HU, Jensen TS. Using screening tools to identify neuropathic pain. Pain. 2007;127(3):199–203.
12. Bouhassira D, Attal N. Diagnosis and assessment of neuropathic pain: the saga of clinical tools. Pain. 2011;152(3 Suppl):S74–83.
13. Andermann A, Blancquaert I, Beauchamp S, Dery V. Revisiting Wilson and Jungner in the genomic age: a review of screening criteria over the past 40 years. Bull World Health Organ. 2008;86(4):317–9.
14. Wilson JM, Jungner YG. Principles and practice of mass screening for disease. Bol Oficina Sanit Panam. 1968;65(4):281–393.
15. Mathieson S, Maher CG, Terwee CB, Folly de Campos T, Lin CW. Neuropathic pain screening questionnaires have limited measurement properties. A systematic review. J Clin Epidemiol. 2015;68(8):957–66. 16. De Andres J, Perez-Cajaraville J, Lopez-Alarcon MD, Lopez-Millan JM,
Margarit C, Rodrigo-Royo MD, Franco-Gay ML, Abejon D, Ruiz MA, Lopez-Gomez V, et al. Cultural adaptation and validation of the painDETECT scale into Spanish. Clin J Pain. 2012;28(3):243–53.
17. Alkan H, Ardic F, Erdogan C, Sahin F, Sarsan A, Findikoglu G. Turkish version of the painDETECT questionnaire in the assessment of neuropathic pain: a validity and reliability study. Pain Med. 2013;14(12):1933–43.
18. Matsubayashi Y, Takeshita K, Sumitani M, Oshima Y, Tonosu J, Kato S, Ohya J, Oichi T, Okamoto N, Tanaka S. Validity and reliability of the Japanese version of the painDETECT questionnaire: a multicenter observational study. PLoS One. 2013;8(9):e68013.
19. Gudala K, Ghai B, Bansal D. Neuropathic pain assessment with the PainDETECT questionnaire: cross-cultural adaptation and psychometric evaluation to Hindi. Pain practice : the official journal of World Institute of Pain. 2017;17(8):1042-49. 20. Sung JK, Choi JH, Jeong J, Kim WJ, Lee DJ, Lee SC, Kim YC, Moon JY. Korean
version of the painDETECT questionnaire: a study for cultural adaptation and validation. Pain practice : the official journal of World Institute of Pain. 2017;17(4):494–504.
21. Freynhagen R, Tolle TR, Gockel U, Baron R. The painDETECT project - far more than a screening tool on neuropathic pain. Curr Med Res Opin. 2016:1–25. 22. Jespersen A, Amris K, Bliddal H, Andersen S, Lavik B, Janssen H, Poulsen PB.
Is neuropathic pain underdiagnosed in musculoskeletal pain conditions? The Danish PainDETECTive study. Curr Med Res Opin. 2010;26(8):2041–5. 23. Catch-22.https://www.merriam-webster.com/dictionary/catch-22. Accessed
26 June 2018.
24. Timmerman H, Wolff AP, Schreyer T, Outermans J, Evers AW, Freynhagen R, Wilder-Smith OH, van Zundert J, Vissers KC. Cross-cultural adaptation to the Dutch language of the PainDETECT-questionnaire. Pain practice : the official journal of World Institute of Pain. 2013;13(3):206-14.
25. Timmerman H, Wilder-Smith O, van Weel C, Wolff A, Vissers K. Detecting the neuropathic pain component in the clinical setting: a study protocol for validation of screening instruments for the presence of a neuropathic pain component. BMC Neurol. 2014;14(1):94.
26. Timmerman H, Steegers MAH, Huygen F, Goeman JJ, van Dasselaar NT, Schenkels MJ, Wilder-Smith OHG, Wolff AP, Vissers KCP. Investigating the validity of the DN4 in a consecutive population of patients with chronic pain. PLoS One. 2017;12(11):e0187961.
27. ICD-10: version. 2015 International Statistical Classification of Diseases and Related Health Problems 10th Revision.http://apps.who.int/classifications/ icd10/browse/2015/en. Accessed 26 June 2018.
28. Lavand'homme P, Thienpont E. Pain after total knee arthroplasty: a narrative review focusing on the stratification of patients at risk for persistent pain. Bone Joint J. 2015;97-B(10 Suppl A):45–8.
29. Cruccu G, Sommer C, Anand P, Attal N, Baron R, Garcia-Larrea L, Haanpaa M, Jensen TS, Serra J, Treede RD. EFNS guidelines on neuropathic pain assessment: revised 2009. Eur J Neurol. 2010;17(8):1010–8.
30. Cruccu G, Anand P, Attal N, Garcia-Larrea L, Haanpaa M, Jorum E, Serra J, Jensen TS. EFNS guidelines on neuropathic pain assessment. Eur J Neurol. 2004;11(3):153–62.
31. Treede RD, Jensen TS, Campbell JN, Cruccu G, Dostrovsky JO, Griffin JW, Hansson P, Hughes R, Nurmikko T, Serra J. Neuropathic pain: redefinition and a grading system for clinical and research purposes. Neurol. 2008;70(18):1630–5. 32. Timmerman H, Heemstra I, Schalkwijk A, Verhagen C, Vissers K, Engels Y.
Assessment of neuropathic pain in patients with Cancer: the Interobserver reliability. An Observational Study in Daily Practice. Pain physician. 2013;16:11.
33. Vaegter HB, Andersen PG, Madsen MF, Handberg G, Enggaard TP. Prevalence of neuropathic pain according to the IASP grading system in patients with chronic non-malignant pain. Pain Med. 2014;15(1):120–7. 34. Tampin B, Briffa NK, Goucke R, Slater H. Identification of neuropathic pain in
patients with neck/upper limb pain: application of a grading system and screening tools. Pain. 2013;154(12):2813–22.
35. Salen BA, Spangfort EV, Nygren AL, Nordemar R. The disability rating index: an instrument for the assessment of disability in clinical settings. J Clin Epidemiol. 1994;47(12):1423–35.
36. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67(6):361–70.
37. Spinhoven P, Ormel J, Sloekers PP, Kempen GI, Speckens AE, Van Hemert AM. A validation study of the hospital anxiety and depression scale (HADS) in different groups of Dutch subjects. Psychol Med. 1997;27(2):363–70. 38. Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the hospital
anxiety and depression scale. An updated literature review. J Psychosom Res. 2002;52(2):69–77.
39. Hays RD, Sherbourne CD, Mazel RM. The RAND 36-item health survey 1.0. Health Econ. 1993;2(3):217–27.
40. VanderZee KI, Sanderman R, Heyink J. A comparison of two
multidimensional measures of health status: the Nottingham health profile and the RAND 36-item health survey 1.0. Qual Life Res. 1996;5(1):165–74. 41. VanderZee KI, Sanderman R, Heyink JW, de Haes H. Psychometric qualities
of the RAND 36-item health survey 1.0: a multidimensional measure of general health status. Int J Behav Med. 1996;3(2):104–22.
42. Collins SL, Edwards J, Moore RA, Smith LA, McQuay HJ. Seeking a simple measure of analgesia for mega-trials: is a single global assessment good enough? Pain. 2001;91(1–2):189–94.
43. Farrar JT, Young JP Jr, LaMoreaux L, Werth JL, Poole RM. Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale. Pain. 2001;94(2):149–58.
44. Fischer D, Stewart AL, Bloch DA, Lorig K, Laurent D, Holman H. Capturing the patient's view of change as a clinical outcome measure. Jama. 1999; 282(12):1157–62.
45. Flahault A, Cadilhac M, Thomas G. Sample size calculation should be performed for design accuracy in diagnostic test studies. J Clin Epidemiol. 2005;58(8):859–62.
46. How good is that test? II.http://www.bandolier.org.uk/band27/b27-2.html. Accessed 26 June 2018.
47. Greenhalgh T. How to read a paper: papers that report diagnostic or screening tests (vol 315, pg 540, 1997). Br Med J. 1997;315(7113):942. 48. Landis JR, Koch GG. The measurement of observer agreement for
categorical data. Biometrics. 1977;33(1):159–74.
49. Heberle H, Meirelles GV, da Silva FR, Telles GP, Minghim R. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics. 2015;16:169.
50. Hallstrom H, Norrbrink C. Screening tools for neuropathic pain: can they be of use in individuals with spinal cord injury? Pain. 2011;152(4):772–9. 51. Gauffin J, Hankama T, Kautiainen H, Hannonen P, Haanpaa M. Neuropathic
pain and use of PainDETECT in patients with fibromyalgia: a cohort study. BMC Neurol. 2013;13:21.
52. Finnerup NB, Haroutounian S, Kamerman P, Baron R, Bennett DL, Bouhassira D, Cruccu G, Freeman R, Hansson P, Nurmikko T, et al. Neuropathic pain: an updated grading system for research and clinical practice. Pain. 2016;157(8): 1599-606.
53. Scholz J, Mannion RJ, Hord DE, Griffin RS, Rawal B, Zheng H, Scoffings D, Phillips A, Guo J, Laing RJ, et al. A novel tool for the assessment of pain: validation in low back pain. PLoS Med. 2009;6(4):e1000047.
54. CADTH. Diagnostic methods for neuropathic pain: A review of diagnostic accuracy. In: Canadian Agency for Drugs and Technologies in Health. Canada: Ottawa (ON); 2015.https://www.ncbi.nlm.nih.gov/pubmedhealth/ PMH0078647/. Accessed 26 June 2018.
55. Bouhassira D, Attal N. Translational neuropathic pain research: a clinical perspective. Neuroscience. 2016;3:27-35.
56. Cooney MA, Culleton-Quinn E, Stokes E. Current knowledge of pain after breast cancer treatment: a systematic review. Pain Manag Nurs. 2013;14(2): 110–23.
57. Ilhan E, Chee E, Hush J, Moloney N. The prevalence of neuropathic pain is high following treatment for breast cancer: a systematic review. Pain. 2017; 15(11):2082-91.
